Predicting User’s Multi-Interests With Network Embedding in Health-Related Topics
Zhipeng Jin1; Ruoran Liu1; Qiudan Li1; Daniel D. Zeng1; YongCheng Zhan2; Lei Wang1,2
Conference Name2016 International Joint Conference on Neural Networks
Source PublicationIJCNN2016
Conference Date24-29 July 2016
Conference PlaceVancouver, Canada
Other AbstractWith the rapid growth of Web 2.0, social media has become a prevalent information sharing and seeking channel for health surveillance, in which users form interactive networks by posting and replying messages, providing and rating reviews, attending multiple discussion boards on health-related topics. Users’ behaviors in these interactive networks reflect users’ multiple interests. To provide better information service for users, it is necessary to analyze the user interactions and predict users’ multi-interests. Most existing work in predicting users’ multi-interests based on multi label network classification focuses on using approximate inference methods to leverage the dependency information to improve classification results. Inspired by deep learning techniques, DEEPWARK learns label independent latent representations of vertices in a network using local information obtained from truncated random walks, which provides an efficient way for predicting users multi-interests from user interactions.  In this paper, we develop a user’s multi-interests prediction model based on DEEPWALK, weight information of user interactions is considered when modeling a stream of short constrained random walks and SkipGram is employed to generate more accurate representations of user vertices, which help identify users’ interests. Experimental results on two real world health-related datasets show the efficacy of the proposed model.
KeywordUser Interaction Network Multi-interest Prediction Weight Information
Document Type会议论文
Corresponding AuthorQiudan Li
Affiliation1.The State Key Laboratory of Management and Control for Complex Systems Institute of Automation, Chinese Academy of Sciences Beijing 100190, China
2.Department of Management Information Systems University of Arizona Tucson, Arizona, USA
Recommended Citation
GB/T 7714
Zhipeng Jin,Ruoran Liu,Qiudan Li,等. Predicting User’s Multi-Interests With Network Embedding in Health-Related Topics[C],2016.
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